Abstract
In definitional question answering (QA), it is essential to rank the candidate answers. In this paper, we propose an online learning algorithm, which dynamically construct the supervisor to reduce the adverse effects of the large number of bad answers and noisy data. We compare our method with two state-of-the-art definitional QA systems and two ranking algorithms, and the experimental results show our method outperforms the others.
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Acknowledgments
We would like to thank the anonymous reviewers for their valuable comments. This work was partially funded by the National Natural Science Foundation of China (61472088, 61363032), the National High Technology Research and Development Program of China (2015AA015408), Shanghai Science and Technology Development Funds (14ZR1403200).
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Wu, S., Qiu, X., Huang, X., Cao, J. (2015). Learning to Rank Answers for Definitional Question Answering. In: Sun, M., Liu, Z., Zhang, M., Liu, Y. (eds) Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data. CCL NLP-NABD 2015 2015. Lecture Notes in Computer Science(), vol 9427. Springer, Cham. https://doi.org/10.1007/978-3-319-25816-4_26
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DOI: https://doi.org/10.1007/978-3-319-25816-4_26
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